Most of the valves the average person sees in daily life are manually operated. For example, the faucet on your kitchen sink is controlled by a water valve. Moving the handle opens the valve to allow water to flow. Closing the valve handle stops the water from flowing. Different valves can be used to control different water flows. For example, a hot water valve can control hot water flow, and a cold water valve can control cold water flow.
A modern aircraft makes wide use of valves. Such valves can be employed to regulate process variables such as fluid flow, temperature and other. Typically however, instead of being actuated manually, the valves are controlled electrically. Often, pneumatic valves are connected in closed loop control systems. Generally speaking, in a modern pneumatic control loop, a fluid flow or other sensor downstream of the pneumatic valve monitors some characteristic of the flow of the fluid the valve controls. Monitored characteristics are fed back to a microprocessor or other circuit that is used to electrically control the valve opening. Very precise control of process parameters can be achieved using such modern pneumatic control loops.
Valve degradation and failure can be a significant problem in applications such as aircraft and industry that depend on proper operation of pneumatic value control systems. It is therefore generally desirable to be able to automatically monitor the health of pneumatic valve control systems. This can be especially valuable in aircraft and other contexts where it is not always convenient to inspect valve operation (e.g., during flight). Abnormal operation may indicate for example that significant degradation is taking place at the valve that can lead to functional failure in the future. Failure or degradation mechanisms can be, among others for example, abnormal friction levels leading to excessive wear between moving parts; air tubing clogging due to the deposition of contaminants; mechanical fatigue and rupture; uncontrolled air leakage at points that are subjected to different pressures; and other phenomena.
Sensors within the valves and along the controlled process may provide measurements that are useful not only for the process control loop, but also to identify abnormal operation and perform valve health monitoring. Besides measurements from process variables, a system's controller can make use of specific sensors to measure internal valve states, such as internal pressures and actuator positions. Internal sensors dedicated to the specific function of monitoring the health of pneumatic valves and their associated control loops can be used to allow more precise detection and isolation of failure modes of valve internal components. While many newer pneumatic valves include such internal sensors, older valve designs that may already be installed in the field often do not. Such internal sensors are thus rarely present in legacy designs due to functional restrictions, cost constraints, or other factors.
Measurements within the controlled process may also be affected by abnormal valve operation. Thus, such measurements can be used to provide indirect indications of the health of the valve. However, indirect measurements may not necessarily allow the root failure mode to be isolated, and are more subject to external disturbances that can mask degradation effects. By way of simple illustration, a decrease in water flow from the end of a garden hose could be attributable to wear of the water valve, but it also could be attributable to decrease in water pressure or a kink in the hose.
Some have used complex mathematics and dynamic system modeling to analytically estimate valve, controller and process states. The initial states of these components, system inputs and disturbances are recorded and fed into a processor that creates a dynamic model of how the system is expected to operate. Differences between the real system state and the state the model expects can be analyzed and translated into valve health estimations. Additional failure propagation and degradation evolution models provide a way to discover the effects of failure modes and to identify failure modes in a faulty system. A disadvantage of this approach is that models may not properly characterize the system due to incompleteness, inaccuracy or random parameter deviations of real systems from modeled ones. An additional disadvantage is that the modeling approach is complex. Anything so complex can introduce its own errors which can be mistaken for system errors. It is possible for a faulty diagnostic system to indicate a fault when there is no fault. Thus, while such modeling is useful, this approach has limitations. Further improvements are possible and desirable.
The exemplary illustrative non-limiting technology herein uses another approach to diagnosing problems: comparing signals and other parameters from two identical or similar processes operating under the same or similar conditions. If plural identical systems are subjected to the same environmental and operating conditions, one can expect their measured states to be very similar. Observed differences in measured states can therefore be associated with degradation taking place in one of the systems. Experience shows that it is relatively or extremely unlikely that both systems will present significant functional degradation at the same time. Thus, such differences can be used to identify a degraded system and to estimate its health.
In accordance with one aspect of exemplary illustrative non-limited implementations herein, measurements from identical valves operating under the same or similar conditions are compared. Differences are translated into estimates of individual valve degradation state. Historical degradation states can be used to predict expected time to failure. An exemplary illustrative non-limiting implementation for assessing the health of a pneumatic valve controlled system onboard an aircraft comprises monitoring the state of a first process controlled by a first pneumatic valve; monitoring the state of a second process controlled by a second pneumatic valve; comparing the monitored state of the first process with the monitored state of the second process to derive at least one comparison result; and ascertaining at least one parameter associated with the health of at least one of the first pneumatic valve and the second pneumatic valve in response at least in part to said comparison result.